Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, the authors develop a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay has achieved 12-plexed biomarker detection in sample volume < 15 μL at concentrations < 5pg/mL while only requiring a 5-min assay incubation, allowing for all processes from sampling to result to be completed within 40 min. The assay procedure applies both a spatial-spectral microfluidic encoding scheme and an image data analysis algorithm based on machine learning with a convolutional neural network (CNN) for pre-equilibrated single-molecule protein digital counting. This unique approach remarkably reduces errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 12 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy reveals the powerful biomarker profiling capability. The assay could also be deployed for near-real-time immune status monitoring of critically ill COVID-19 patients developing cytokine storm syndrome.

Digital protein assays have great potential to advance immunodiagnostics because of their single-molecule sensitivity, high precision, and robust measurements. However, translating digital protein assays to acute clinical care has been challenging because it requires their deployment with a rapid turnaround. Herein, we present a technology platform for ultra-fast digital protein biomarker detection by employing single-molecule counting of immune-complex formation events at an early, pre-equilibrium state. This method, which we term "pre-equilibrium digital enzyme-linked immunosorbent assay" (PEdELISA), can quantify a multiplexed panel of protein biomarkers in 10 µL of serum within an unprecedented assay incubation time of 15-300 sec over a 104 dynamic range. PEdELISA allowed us to perform rapid monitoring of protein biomarkers in patients manifesting post-chimeric antigen receptor T-cell (CAR-T) therapy cytokine release syndrome (CRS), with ~30 min sample-to-answer time and a sub-pg/mL limit of detection (LOD). The rapid, sensitive, and low input volume biomarker quantification enabled by PEdELISA is broadly applicable to timely monitoring of acute disease, potentially enabling more personalized treatment.

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Advanced in vitro tissue chip models can reduce and replace animal experimentation and may eventually support “on‐chip” clinical trials. To realize this potential, however, tissue chip platforms must be both mass‐produced and reconfigurable to allow for customized design. To address these unmet needs, an extension of the µSiM (microdevice featuring a silicon‐nitride membrane) platform is introduced. The modular µSiM (m‐µSiM) uses mass‐produced components to enable rapid assembly and reconfiguration by laboratories without knowledge of microfabrication. The utility of the m‐µSiM is demonstrated by establishing an hiPSC‐derived blood–brain barrier (BBB) in bioengineering and nonengineering, brain barriers focused laboratories. In situ and sampling‐based assays of small molecule diffusion are developed and validated as a measure of barrier function. BBB properties show excellent interlaboratory agreement and match expectations from literature, validating the m‐µSiM as a platform for barrier models and demonstrating successful dissemination of components and protocols. The ability to quickly reconfigure the m‐µSiM for coculture and immune cell transmigration studies through addition of accessories and/or quick exchange of components is then demonstrated. Because the development of modified components and accessories is easily achieved, custom designs of the m‐µSiM shall be accessible to any laboratory desiring a barrier‐style tissue chip platform.

Advanced in vitro tissue chip models can reduce and replace animal experimentation and may eventually support 'on-chip' clinical trials. To realize this potential, however, tissue chip platforms must be both mass-produced and reconfigurable to allow for customized design. To address these unmet needs, we introduce an extension of our μSiM (microdevice featuring a silicon-nitride membrane) platform. The modular μSiM (m-μSiM) uses mass-produced components to enable rapid assembly and reconfiguration by laboratories without knowledge of microfabrication. We demonstrate the utility of the m-μSiM by establishing an hiPSC-derived blood-brain barrier (BBB) in bioengineering and non-engineering, brain barriers focused laboratories. We develop and validate in situ and sampling-based assays of small molecule diffusion as a measure of barrier function. BBB properties show excellent interlaboratory agreement and match expectations from literature, validating the m-μSiM as a platform for barrier models and demonstrating successful dissemination of components and protocols. We then demonstrate the ability to quickly reconfigure the m-μSiM for co-culture and immune cell transmigration studies through addition of accessories and/or quick exchange of components. Because the development of modified components and accessories is easily achieved, custom designs of the m-μSiM should be accessible to any laboratory desiring a barrier-style tissue chip platform.

No abstract

Integrated microfluidic cellular phenotyping platforms provide a promising means of studying a variety of inflammatory diseases mediated by cell‐secreted cytokines. However, immunosensors integrated in previous microfluidic platforms lack the sensitivity to detect small signals in the cellular secretion of proinflammatory cytokines with high precision. This limitation prohibits researchers from studying cells secreting cytokines at low abundance or existing at a small population. Herein, the authors present an integrated platform named the “digital Phenoplate (dPP),” which integrates digital immunosensors into a microfluidic chip with on‐chip cell assay chambers, and demonstrates ultrasensitive cellular cytokine secretory profile measurement. The integrated sensors yield a limit of detection as small as 0.25 pg mL−1 for mouse tumor necrosis factor alpha (TNF‐α). Each on‐chip cell assay chamber confines cells whose population ranges from ≈20 to 600 in arrayed single‐cell trapping microwells. Together, these microfluidic features of the dPP simultaneously permit precise counting and image‐based cytometry of individual cells while performing parallel measurements of TNF‐α released from rare cells under multiple stimulant conditions for multiple samples. The dPP platform is broadly applicable to the characterization of cellular phenotypes demanding high precision and high throughput.

Despite widespread concern for cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4pg/mL), inter-assay repeatability (~10% CV), and near-real-time operation with a 10 min assay incubation. A cytokine profiling test with the platform allowed us to observe clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as Ferritin and CRP. Our data revealed large subject-to-subject variability in a patient's response to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.